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Graph Representation Learning for Wireless Communications
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DOI：10.1109/MCOM.001.2200810.png)
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Abstract
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Wireless networks are inherently graph-structured in which graph representation learning can be utilized to solve complex network optimization problems. In graph representation learning, feature vectors for each entity in the network are calculated such that they could capture spatial and temporal dependencies in their local and global neighborhoods. Specifically, graph neural networks (GNNs) are powerful tools to solve these complex problems because of their expressive representation and reasoning power. In this article, the potential of graph representation learning and GNNs in wireless networks is presented. An overview of graph representation learning is provided which covers the fundamentals and concepts, such as feature design over graphs, GNNs, and their design principles. The potential of graph representation learning in wireless networks is presented via a few exemplary use cases and some initial results on the GNN-based access point selection for cell-free massive Multiple-Input Multiple-Output (MIMO) systems.
Keywords:
Representation learning
Deep learning
Wireless networks
Feature extraction
Neural networks
Task analysis
Optimization
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